Jekaterina (Kat) Pasecnika

Experienced and passionate data scientist with an academic background in mathematics and actuarial science.

I am looking for opportunities within both Data Science and Analytics in general as well as roles adjacent to those. 

Having had experience in variety of industries and teams I am capable of picking up and applying new concepts on the go. 

Data Scientist
Riga, Latvia
[email protected]

Key Skills

  • Python 
  • Azure ( Including Azure ML) and AWS
  • Databricks
  • Pyspark
  • SQL 
  • Neo4j
  • Tableau 
  • Knime
  • DevOps


  • Supervised Learning (Classification algorithms, Regressions, Text analytics/NLP)
  • Unsupervised Learning (Clustering, PCA)
  • Optimization Solvers (LP)
  • Deep Learning (Image classification)
  • Machine learning using Azure ML


  • Attention for detail 
  • Explaining technical concepts to non-technical audience
  • Creative Thinking 
  • Agile way of working
  • Team work 
  • Stakeholder Management

Work Achievements

Conjura, Senior Data Scientist, Aug 2021 ~ April 2022

Delivering bespoke Data Science solution to e-commerce clients by:

  • Developing use-cases directly with customers and steer implementations to drive at the most valuable outcomes. 
    • Developed bespoke churn and LTV model for an e-commerce customer that helped drive retention and more deliberate targeting.
    • Developed an RFM based segmentation for an e-commerce business to help understand their customer base at an initial level prior to developing a churn/LTV model.


Mars Inc., Data Scientist, Sep 2019 ~ June 2021

At Mars I was a part of growing the Analytics capability within supply chain function.

Benefits that I have been bringing :

  • Leading a cost performance management initiative project that has leveraged ML to build a Root Cause Analysis (RCA) framework and the models that will be later used for “What-if” scenarios and future cost predictions. The project’s expected outcome for US Pet sector is $2.3M Cost of Goods Sold benefit and $7M $GSV lift (Lasso Regression Model)
  • Minimising the losses brought by unmet demand or too much excess stock by  leading  production optimisation project . The solution will also help cut the costs of using a third party solver and front end used before, as all of the aspects are being built in house. (Linear Optimisation solver)
  • Better visibility into our range of products that eventually enables Demand Planners to efficiently plan and priorities products in different segments by using Data Science Segmentation techniques. (Clustering Algorithms)
  • Tool to drive Drive factory closure/associates safety decision making process acceleration and delivery at critical times.24 hour sprints delivered at the time of the Global COVID-19 pandemic outbreak (March-April 2020) that allowed the business to have the most up to date front end risk estimation tool to.(Logistic curve based predictions and other internal associates data based risk calculations)
Other Initiatives: 
  • I started developing my own training program on Machine learning as an introduction for the Senior Business Leaders of Mars Supply Chain to help drive the education on ML in the business as a whole.


Virgin Media, Data Scientist, Jul 2018 ~ Aug 2019

Championing the Data Science Function within Data Intelligence team by :

  • Improving the rate of meaningful contact with a customer which resulted in an increase of retention by building customer contact strategy model (XG Boost model)
  • Churn preventative measures design by building a view of a customer's journey and identifying trigger points within that journey (Graph databases - Neo4j)
Other things I influenced:
  • Collaboration and building relationships across the business and other data teams by organizing Monthly Analyst Meetings within the department.
  • Education of other non-technical members of the business on Data Science by hosting a regular Data Science Catch-ups.
  • Exploration of new tools and techniques that have potential to be beneficial in delivering Data Science projects more efficiently. - Successfully brought tools like Knime (graph database) into the team.


British Gas, Customer Value Management (CVM) Analyst, Nov 2017 ~ Jun 2018

As a customer focused analyst, I influenced :

  • Course correction actions by producing summary statistics on campaign impact, rationale and commentary for departmental activities. (SQL Databases)
  • Providing an assessment of value added/lost across the base each month and contribution of CVM activities. ( SQL Databases)


Thatcham Research, Analyst/Statistician, May 2016 ~ Nov 2017

Working at Thatcham Research, my achievements were:

  • Development of statistical models and forecasts/predictions to evaluate risks of new and emerging autonomous vehicle technologies by using the latest trends in motor claims and repairs. (Modelling approaches mainly including GLM)
  • Drive our Motor Insurance Stakeholders initiative to understand UK car accidents in a new more meaningful way to understand most common weather/road/traffic conditions of the accidents by segmenting UK road accidents into most meaningful groups. (Cluster Analysis)


Ericsson, Performance Analyst, Jan 2012 ~ Apr 2016

As a young analyst at Ericsson my first key projects were:

  • Driving cost saving for the business by pointing at most obvious "risk" areas of overspent that  could be mitigated/avoided by examining trends and most common Telecoms network trouble tickets scenarios . (SQL Database querying + Access DB + Excel)
  • Developing new performance indicators and insights for the business in order to help see broader pictures of project performance. (SQL Database querying + Access DB + Excel )



Udacity Nano Degree - Data Science For Enterprise, 2019 ~ 2020

Modules covered: Supervised learning, Unsupervised Learning, Deep learning, Software engineering, Data Engineering, A/B testing, Recommendation Engines.

Relevant software/language used: Python (Pandas, Scikit-learn, Pytorch, Keras, Beautiful Soup, Flask, NLTK, others),  HTML, CSS, Heroku


University of Southampton, Master of Science (MS), Actuarial Science (1st class degree), 2014 ~ 2015

Modules covered: Financial Mathematics, Survival Models, Actuarial Mathematics, Economics (Macro and Microeconomics), Statistical Methods in Insurance, Stochastic Processes and Accounting.
Relevant projects: MSc dissertation “Mortality modelling and projection” – Studying stochastic models for human mortality and finding best model for each selected country.
Relevant software/language used: R (Extensive use throughout the year and when working on the dissertation).


University of Southampton, Bachelor of Science (BS), Mathematics with Actuarial Science, 2009 ~ 2012


Interests and Hobbies

  • Volunteering for DataKind specifically Data competitions (Datadives, Datathons) (If you haven't come across Datakind, definitely check them out)
  • Chess - still most favorite game of all times since age of 5 
  • Table tennis - a passion of mine since school days !
  • Watercolor sketching  that turned into interesting side projects of making and selling our own yearly planners/notebooks :)

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